Báo cáo khoa học: "Personalizing PageRank for Word Sense Disambiguation" docx

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 33–41,
Athens, Greece, 30 March – 3 April 2009.
c
2009 Association for Computational Linguistics
Personalizing PageRank for Word Sense Disambiguation
Eneko Agirre and Aitor Soroa
IXA NLP Group
University of the Basque Country
Donostia, Basque Contry
{e.agirre,a.soroa}@ehu.es
Abstract
In this paper we propose a new graph-
based method that uses the knowledge in
a LKB (based on WordNet) in order to
perform unsupervised Word Sense Disam-
biguation. Our algorithm uses the full
graph of the LKB efficiently, performing
better than previous approaches in English
all-words datasets. We also show that the
algorithm can be easily ported to other lan-
guages with good results, with the only re-
quirement of having a wordnet. In addi-
tion, we make an analysis of the perfor-
mance of the algorithm, showing that it is
efficient and that it could be tuned to be
faster.
1 Introduction
Word Sense Disambiguation (WSD) is a key
enabling-technology that automatically chooses
the intended sense of a word in context. Super-
vised WSD systems are the best performing in

public evaluations (Palmer et al., 2001; Snyder
and Palmer, 2004; Pradhan et al., 2007) but they
need large amounts of hand-tagged data, which is
typically very expensive to build. Given the rela-
tively small amount of training data available, cur-
rent state-of-the-art systems only beat the simple
most frequent sense (MFS) baseline
1
by a small
margin. As an alternative to supervised systems,
knowledge-based WSD systems exploit the infor-
mation present in a lexical knowledge base (LKB)
to perform WSD, without using any further corpus
evidence.
1
This baseline consists of tagging all occurrences in the
test data with the sense of the word that occurs more often in
the training data
Traditional knowledge-based WSD systems as-
sign a sense to an ambiguous word by comparing
each of its senses with those of the surrounding
context. Typically, some semantic similarity met-
ric is used for calculating the relatedness among
senses (Lesk, 1986; McCarthy et al., 2004). One
of the major drawbacks of these approaches stems
from the fact that senses are compared in a pair-
wise fashion and thus the number of computa-
tions can grow exponentially with the number of
words. Although alternatives like simulated an-
nealing (Cowie et al., 1992) and conceptual den-

sity (Agirre and Rigau, 1996) were tried, most of
past knowledge based WSD was done in a subop-
timal word-by-word process, i.e., disambiguating
words one at a time.
Recently, graph-based methods for knowledge-
based WSD have gained much attention in the
NLP community (Sinha and Mihalcea, 2007; Nav-
igli and Lapata, 2007; Mihalcea, 2005; Agirre
and Soroa, 2008). These methods use well-known
graph-based techniques to find and exploit the
structural properties of the graph underlying a par-
ticular LKB. Because the graph is analyzed as a
whole, these techniques have the remarkable prop-
erty of being able to find globally optimal solu-
tions, given the relations between entities. Graph-
based WSD methods are particularly suited for
disambiguating word sequences, and they man-
age to exploit the interrelations among the senses
in the given context. In this sense, they provide
a principled solution to the exponential explosion
problem, with excellent performance.
Graph-based WSD is performed over a graph
composed by senses (nodes) and relations between
pairs of senses (edges). The relations may be of
several types (lexico-semantic, coocurrence rela-
tions, etc.) and may have some weight attached to
33
them. The disambiguation is typically performed
by applying a ranking algorithm over the graph,
and then assigning the concepts with highest rank

to the corresponding words. Given the compu-
tational cost of using large graphs like WordNet,
many researchers use smaller subgraphs built on-
line for each target context.
In this paper we present a novel graph-based
WSD algorithm which uses the full graph of
WordNet efficiently, performing significantly bet-
ter that previously published approaches in En-
glish all-words datasets. We also show that the
algorithm can be easily ported to other languages
with good results, with the only requirement of
having a wordnet. The algorithm is publicly avail-
able
2
and can be applied easily to sense invento-
ries and knowledge bases different from WordNet.
Our analysis shows that our algorithm is efficient
compared to previously proposed alternatives, and
that a good choice of WordNet versions and rela-
tions is fundamental for good performance.
The paper is structured as follows. We first de-
scribe the PageRank and Personalized PageRank
algorithms. Section 3 introduces the graph based
methods used for WSD. Section 4 shows the ex-
perimental setting and the main results, and Sec-
tion 5 compares our methods with related exper-
iments on graph-based WSD systems. Section 6
shows the results of the method when applied to
a Spanish dataset. Section 7 analyzes the perfor-
mance of the algorithm. Finally, we draw some

conclusions in Section 8.
2 PageRank and Personalized PageRank
The celebrated PageRank algorithm (Brin and
Page, 1998) is a method for ranking the vertices
in a graph according to their relative structural
importance. The main idea of PageRank is that
whenever a link from v
i
to v
j
exists in a graph, a
vote from node i to node j is produced, and hence
the rank of node j increases. Besides, the strength
of the vote from i to j also depends on the rank
of node i: the more important node i is, the more
strength its votes will have. Alternatively, PageR-
ank can also be viewed as the result of a random
walk process, where the final rank of node i rep-
resents the probability of a random walk over the
graph ending on node i, at a sufficiently large time.
Let G be a graph with N vertices v
1
, . . . , v
N
and d
i
be the outdegree of node i; let M be a
2
/>N ×N transition probability matrix, where M
ji

=
1
d
i
if a link from i to j exists, and zero otherwise.
Then, the calculation of the PageRank vector Pr
over G is equivalent to resolving Equation (1).
Pr = cMPr + (1 − c)v (1)
In the equation, v is a N × 1 vector whose ele-
ments are
1
N
and c is the so called damping factor,
a scalar value between 0 and 1. The first term of
the sum on the equation models the voting scheme
described in the beginning of the section. The sec-
ond term represents, loosely speaking, the proba-
bility of a surfer randomly jumping to any node,
e.g. without following any paths on the graph.
The damping factor, usually set in the [0.85 0.95]
range, models the way in which these two terms
are combined at each step.
The second term on Eq. (1) can also be seen as
a smoothing factor that makes any graph fulfill the
property of being aperiodic and irreducible, and
thus guarantees that PageRank calculation con-
verges to a unique stationary distribution.
In the traditional PageRank formulation the vec-
tor v is a stochastic normalized vector whose ele-
ment values are all

1
N
, thus assigning equal proba-
bilities to all nodes in the graph in case of random
jumps. However, as pointed out by (Haveliwala,
2002), the vector v can be non-uniform and assign
stronger probabilities to certain kinds of nodes, ef-
fectively biasing the resulting PageRank vector to
prefer these nodes. For example, if we concen-
trate all the probability mass on a unique node i,
all random jumps on the walk will return to i and
thus its rank will be high; moreover, the high rank
of i will make all the nodes in its vicinity also re-
ceive a high rank. Thus, the importance of node i
given by the initial distribution of v spreads along
the graph on successive iterations of the algorithm.
In this paper, we will use traditional PageRank
to refer to the case when a uniform v vector is used
in Eq. (1); and whenever a modified v is used, we
will call it Personalized PageRank. The next sec-
tion shows how we define a modified v.
PageRank is actually calculated by applying an
iterative algorithm which computes Eq. (1) suc-
cessively until convergence below a given thresh-
old is achieved, or, more typically, until a fixed
number of iterations are executed.
Regarding PageRank implementation details,
we chose a damping value of 0.85 and finish the
calculation after 30 iterations. We did not try other
34

damping factors. Some preliminary experiments
with higher iteration counts showed that although
sometimes the node ranks varied, the relative order
among particular word synsets remained stable af-
ter the initial iterations (cf. Section 7 for further
details). Note that, in order to discard the effect
of dangling nodes (i.e. nodes without outlinks) we
slightly modified Eq. (1). For the sake of brevity
we omit the details, which the interested reader
can check in (Langville and Meyer, 2003).
3 Using PageRank for WSD
In this section we present the application of
PageRank to WSD. If we were to apply the tra-
ditional PageRank over the whole WordNet we
would get a context-independent ranking of word
senses, which is not what we want. Given an input
piece of text (typically one sentence, or a small set
of contiguous sentences), we want todisambiguate
all open-class words in the input taken the rest as
context. In this framework, we need to rank the
senses of the target words according to the other
words in the context. Theare two main alternatives
to achieve this:
• To create a subgraph of WordNet which con-
nects the senses of the words in the input text,
and then apply traditional PageRank over the
subgraph.
• To use Personalized PageRank, initializing v
with the senses of the words in the input text
The first method has been explored in the lit-

erature (cf. Section 5), and we also presented a
variant in (Agirre and Soroa, 2008) but the second
method is novel in WSD. In both cases, the algo-
rithms return a list of ranked senses for each target
word in the context. We will see each of them in
turn, but first we will present some notation and a
preliminary step.
3.1 Preliminary step
A LKB is formed by a set of concepts and relations
among them, and a dictionary, i.e., a list of words
(typically, word lemmas) each of them linked to
at least one concept of the LKB. Given any such
LKB, we build an undirected graph G = (V, E)
where nodes represent LKB concepts (v
i
), and
each relation between concepts v
i
and v
j
is rep-
resented by an undirected edge e
i,j
.
In our experiments we have tried our algorithms
using three different LKBs:
• MCR16 + Xwn: The Multilingual Central
Repository (Atserias et al., 2004b) is a lexical
knowledge base built within the MEANING
project

3
. This LKB comprises the original
WordNet 1.6 synsets and relations, plus some
relations from other WordNet versions auto-
matically mapped
4
into version 1.6: WordNet
2.0 relations and eXtended WordNet relations
(Mihalcea and Moldovan, 2001) (gold, silver
and normal relations). The resulting graph
has 99, 632 vertices and 637, 290 relations.
• WNet17 + Xwn: WordNet 1.7 synset and
relations and eXtended WordNet relations.
The graph has 109, 359 vertices and 620, 396
edges
• WNet30 + gloss: WordNet 3.0 synset and
relations, including manually disambiguated
glosses . The graph has 117, 522 vertices and
525, 356 relations.
Given an input text, we extract the list W
i
i =
1 . . . m of content words (i.e. nouns, verbs, ad-
jectives and adverbs) which have an entry in the
dictionary, and thus can be related to LKB con-
cepts. Let Concepts
i
= {v
1
, . . . , v

i
m
} be the
i
m
associated concepts of word W
i
in the LKB
graph. Note that monosemous words will be re-
lated to just one concept, whereas polysemous
words may be attached to several. As a result
of the disambiguation process, every concept in
Concepts
i
, i = 1, . . . , m receives a score. Then,
for each target word to be disambiguated, we just
choose its associated concept in G with maximal
score.
In our experiments we build a context of at least
20 content words for each sentence to be disam-
biguated, taking the sentences immediately before
and after it in the case that the original sentence
was too short.
3.2 Traditional PageRank over Subgraph
(Spr)
We follow the algorithm presented in (Agirre and
Soroa, 2008), which we explain here for complete-
ness. The main idea of the subgraph method is to
extract the subgraph of G
KB

whose vertices and
relations are particularly relevant for a given input
3
/>4
We use the freely available WordNet mappings from
/>35
context. Such a subgraph is called a “disambigua-
tion subgraph” G
D
, and it is built in the following
way. For each word W
i
in the input context and
each concept v
i
∈ Concepts
i
, a standard breath-
first search (BFS) over G
KB
is performed, start-
ing at node v
i
. Each run of the BFS calculates the
minimum distance paths between v
i
and the rest of
concepts of G
KB
. In particular, we are interested

in the minimum distance paths between v
i
and the
concepts associated to the rest of the words in the
context, v
j
∈

j=i
Concepts
j
. Let mdp
v
i
be the
set of these shortest paths.
This BFS computation is repeated for every
concept of every word in the input context, stor-
ing mdp
v
i
accordingly. At the end, we obtain a
set of minimum length paths each of them hav-
ing a different concept as a source. The disam-
biguation graph G
D
is then just the union of the
vertices and edges of the shortest paths, G
D
=


m
i=1
{mdp
v
j
/v
j
∈ Concepts
i
}.
The disambiguation graph G
D
is thus a sub-
graph of the original G
KB
graph obtained by com-
puting the shortest paths between the concepts of
the words co-occurring in the context. Thus, we
hypothesize that it captures the most relevant con-
cepts and relations in the knowledge base for the
particular input context.
Once the G
D
graph is built, we compute the tra-
ditional PageRank algorithm over it. The intuition
behind this step is that the vertices representing
the correct concepts will be more relevant in G
D
than the rest of the possible concepts of the context

words, which should have less relations on average
and be more isolated.
As usual, the disambiguation step is performed
by assigning to each word W
i
the associated con-
cept in Concepts
i
which has maximum rank. In
case of ties we assign all the concepts with maxi-
mum rank. Note that the standard evaluation script
provided in the Senseval competitions treats mul-
tiple senses as if one was chosen at random, i.e.
for evaluation purposes our method is equivalent
to breaking ties at random.
3.3 Personalized PageRank (Ppr and
Ppr
w2w)
As mentioned before, personalized PageRank al-
lows us to use the full LKB. We first insert the
context words into the graph G as nodes, and link
them with directed edges to their respective con-
cepts. Then, we compute the personalized PageR-
ank of the graph G by concentrating the initial
probability mass uniformly over the newly intro-
duced word nodes. As the words are linked to
the concepts by directed edges, they act as source
nodes injecting mass into the concepts they are as-
sociated with, which thus become relevant nodes,
and spread their mass over the LKB graph. There-

fore, the resulting personalized PageRank vector
can be seen as a measure of the structural rele-
vance of LKB concepts in the presence of the input
context.
One problem with Personalized PageRank is
that if one of the target words has two senses
which are related by semantic relations, those
senses reinforce each other, and could thus
dampen the effect of the other senses in the con-
text. With this observation in mind we devised
a variant (dubbed Ppr
w2w), where we build the
graph for each target word in the context: for each
target word W
i
, we concentrate the initial proba-
bility mass in the senses of the words surrounding
W
i
, but not in the senses of the target word itself,
so that context words increase its relative impor-
tance in the graph. The main idea of this approach
is to avoid biasing the initial score of concepts as-
sociated to target word W
i
, and let the surround-
ing words decide which concept associated to W
i
has more relevance. Contrary to the other two ap-
proaches, Ppr

w2w does not disambiguate all tar-
get words of the context in a single run, which
makes it less efficient (cf. Section 7).
4 Evaluation framework and results
In this paper we will use two datasets for com-
paring graph-based WSD methods, namely, the
Senseval-2 (S2AW) and Senseval-3 (S3AW) all
words datasets (Snyder and Palmer, 2004; Palmer
et al., 2001), which are both labeled with WordNet
1.7 tags. We did not use the Semeval dataset, for
the sake of comparing our results to related work,
none of which used Semeval data. Table 1 shows
the results as recall of the graph-based WSD sys-
tem over these datasets on the different LKBs. We
detail overall results, as well as results per PoS,
and the confidence interval for the overall results.
The interval was computed using bootstrap resam-
pling with 95% confidence.
The table shows that Ppr
w2w is consistently
the best method in both datasets and for all LKBs.
Ppr and Spr obtain comparable results, which is
remarkable, given the simplicity of the Ppr algo-
36
Senseval-2 All Words dataset
LKB Method All N V Adj. Adv. Conf. interval
MCR16 + Xwn Ppr 51.1 64.9 38.1 57.4 47.5 [49.3, 52.6]
MCR16 + Xwn Ppr
w2w 53.3 64.5 38.6 58.3 48.1 [52.0, 55.0]
MCR16 + Xwn Spr 52.7 64.8 35.3 56.8 50.2 [51.3, 54.4]

WNet17 + Xwn Ppr 56.8 71.1 33.4 55.9 67.1 [55.0, 58.7]
WNet17 + Xwn Ppr
w2w 58.6 70.4 38.9 58.3 70.1 [56.7, 60.3]
WNet17 + Xwn Spr 56.7 66.8 37.7 57.6 70.8 [55.0, 58.2]
WNet30 + gloss Ppr 53.5 70.0 28.6 53.9 55.1 [51.8, 55.2]
WNet30 + gloss Ppr
w2w 55.8 71.9 34.4 53.8 57.5 [54.1, 57.8]
WNet30 + gloss Spr 54.8 68.9 35.1 55.2 56.5 [53.2, 56.3]
MFS 60.1 71.2 39.0 61.1 75.4 [58.6, 61.9]
SMUaw 68.6 78.0 52.9 69.9 81.7
Senseval-3 All Words dataset
LKB Method All N V Adj. Adv.
MCR16 + Xwn Ppr 54.3 60.9 45.4 56.5 92.9 [52.3, 56.1]
MCR16 + Xwn Ppr
w2w 55.8 63.2 46.2 57.5 92.9 [53.7, 57.7]
MCR16 + Xwn Static 53.7 59.5 45.0 57.8 92.9 [51.8, 55.7]
WNet17 + Xwn Ppr 56.1 62.6 46.0 60.8 92.9 [54.0, 58.1]
WNet17 + Xwn Ppr
w2w 57.4 64.1 46.9 62.6 92.9 [55.5, 59.3]
WNet17 + Xwn Spr 56.20 61.6 47.3 61.8 92.9 [54.8, 58.2]
WNet30 + gloss Ppr 48.5 52.2 41.5 54.2 78.6 [46.7, 50.6]
WNet30 + gloss Ppr
w2w 51.6 59.0 40.2 57.2 78.6 [49.9, 53.3]
WNet30 + gloss Spr 45.4 54.1 31.4 52.5 78.6 [43.7, 47.4]
MFS 62.3 69.3 53.6 63.7 92.9 [60.2, 64.0]
GAMBL 65.2 70.8 59.3 65.3 100
Table 1: Results (as recall) on Senseval-2 and Senseval-3 all words tasks. We also include the MFS
baseline and the best results of supervised systems at competition time (SMUaw,GAMBL).
rithm, compared to the more elaborate algorithm
to construct the graph. The differences between

methods are not statistically significant, which is a
common problem on this relatively small datasets
(Snyder and Palmer, 2004; Palmer et al., 2001).
Regarding LKBs, the best results are obtained
using WordNet 1.7 and eXtended WordNet. Here
the differences are in many cases significant.
These results are surprising, as we would ex-
pect that the manually disambiguated gloss re-
lations from WordNet 3.0 would lead to bet-
ter results, compared to the automatically disam-
biguated gloss relations from the eXtended Word-
Net (linked to version 1.7). The lower perfor-
mance of WNet30+gloss can be due to the fact
that the Senseval all words data set is tagged using
WordNet 1.7 synsets. When using a different LKB
for WSD, a mapping to WordNet 1.7 is required.
Although the mapping is cited as having a correct-
ness on the high 90s (Daude et al., 2000), it could
have introduced sufficient noise to counteract the
benefits of the hand-disambiguated glosses.
Table 1 also shows the most frequent sense
(MFS), as well as the best supervised sys-
tems (Snyder and Palmer, 2004; Palmer et
al., 2001) that participated in each competition
(SMUaw and GAMBL, respectively). The MFS is
a baseline for supervised systems, but it is consid-
ered a difficult competitor for unsupervised sys-
tems, which rarely come close to it. In this case
the MFS baseline was computed using previously
availabel training data like SemCor. Our best re-

sults are close to the MFS in both Senseval-2 and
Senseval-3 datasets. The results for the supervised
system are given for reference, and we can see that
the gap is relatively small, specially for Senseval-
3.
5 Comparison to Related work
In this section we will briefly describe some
graph-based methods for knowledge-based WSD.
The methods here presented cope with the prob-
lem of sequence-labeling, i.e., they disambiguate
all the words coocurring in a sequence (typically,
all content words of a sentence). All the meth-
ods rely on the information represented on some
LKB, which typically is some version of Word-
Net, sometimes enriched with proprietary rela-
tions. The results on our datasets, when available,
are shown in Table 2. The table also shows the
performance of supervised systems.
The TexRank algorithm (Mihalcea, 2005) for
WSD creates a complete weighted graph (e.g. a
graph where every pair of distinct vertices is con-
nected by a weighted edge) formed by the synsets
of the words in the input context. The weight
37
Senseval-2 All Words dataset
System All N V Adj. Adv.
Mih05 54.2 57.5 36.5 56.7 70.9
Sihna07 56.4 65.6 32.3 61.4 60.2
Tsatsa07 49.2 – – – –
Spr 56.6 66.7 37.5 57.6 70.8

Ppr 56.8 71.1 33.4 55.9 67.1
Ppr
w2w 58.6 70.4 38.9 58.3 70.1
MFS 60.1 71.2 39.0 61.1 75.4
Senseval-3 All Words dataset
System All N V Adj. Adv.
Mih05 52.2 - - - -
Sihna07 52.4 60.5 40.6 54.1 100.0
Nav07 - 61.9 36.1 62.8 -
Spr 56.2 61.6 47.3 61.8 92.9
Ppr 56.1 62.6 46.0 60.8 92.9
Ppr
w2w 57.4 64.1 46.9 62.6 92.9
MFS 62.3 69.3 53.6 63.7 92.9
Nav05 60.4 - - - -
Table 2: Comparison with related work. Note that
Nav05 uses the MFS.
of the links joining two synsets is calculated by
executing Lesk’s algorithm (Lesk, 1986) between
them, i.e., by calculating the overlap between the
words in the glosses of the correspongind senses.
Once the complete graph is built, the PageRank al-
gorithm is executed over it and words are assigned
to the most relevant synset. In this sense, PageR-
ank is used an alternative to simulated annealing
to find the optimal pairwise combinations. The
method was evaluated on the Senseval-3 dataset,
as shown in row Mih05 on Table 2.
(Sinha and Mihalcea, 2007) extends their pre-
vious work by using a collection of semantic sim-

ilarity measures when assigning a weight to the
links across synsets. They also compare differ-
ent graph-based centrality algorithms to rank the
vertices of the complete graph. They use differ-
ent similarity metrics for different POS types and
a voting scheme among the centrality algorithm
ranks. Here, the Senseval-3 corpus was used as
a development data set, and we can thus see those
results as the upper-bound of their method.
We can see in Table 2 that the methods pre-
sented in this paper clearly outperform both Mih05
and Sin07. This result suggests that analyzing the
LKB structure as a whole is preferable than com-
puting pairwise similarity measures over synsets.
The results of various in-house made experiments
replicating (Mihalcea, 2005) also confirm this ob-
servation. Note also that our methods are simpler
than the combination strategy used in (Sinha and
Mihalcea, 2007), and that we did not perform any
parameter tuning as they did.
In (Navigli and Velardi, 2005) the authors de-
velop a knowledge-based WSD method based on
lexical chains called structural semantic intercon-
nections (SSI). Although the system was first de-
signed to find the meaning of the words in Word-
Net glosses, the authors also apply the method for
labeling text sequences. Given a text sequence,
SSI first identifies monosemous words and assigns
the corresponding synset to them. Then, it iter-
atively disambiguates the rest of terms by select-

ing the senses that get the strongest interconnec-
tion with the synsets selected so far. The inter-
connection is calculated by searching for paths on
the LKB, constrained by some hand-made rules of
possible semantic patterns. The method was eval-
uated on the Senseval-3 dataset, as shown in row
Nav05 on Table 2. Note that the method labels
an instance with the most frequent sense of the
word if the algorithm produces no output for that
instance, which makes comparison to our system
unfair, specially given the fact that the MFS per-
forms better than SSI. In fact it is not possible to
separate the effect of SSI from that of the MFS.
For this reason we place this method close to the
MFS baseline in Table 2.
In (Navigli and Lapata, 2007), the authors per-
form a two-stage process for WSD. Given an input
context, the method first explores the whole LKB
in order to find a subgraph which is particularly
relevant for the words of the context. Then, they
study different graph-based centrality algorithms
for deciding the relevance of the nodes on the sub-
graph. As a result, every word of the context is
attached to the highest ranking concept among its
possible senses. The Spr method is very similar
to (Navigli and Lapata, 2007), the main differ-
ence lying on the initial method for extracting the
context subgraph. Whereas (Navigli and Lapata,
2007) apply a depth-first search algorithm over the
LKB graph —and restrict the depth of the subtree

to a value of 3—, Spr relies on shortest paths be-
tween word synsets. Navigli and Lapata don’t re-
port overall results and therefore, we can’t directly
compare our results with theirs. However, we can
see that on a PoS-basis evaluation our results are
consistently better for nouns and verbs (especially
the Ppr
w2w method) and rather similar for adjec-
tives.
(Tsatsaronis et al., 2007) is another example of
a two-stage process, the first one consisting on
finding a relevant subgraph by performing a BFS
38
Spanish Semeval07
LKB Method Acc.
Spanish Wnet + Xnet
∗
Ppr 78.4
Spanish Wnet + Xnet
∗
Ppr
w2w 79.3
– MFS 84.6
– Supervised 85.10
Table 3: Results (accuracy) on Spanish Semeval07
dataset, including MFS and the best supervised
system in the competition.
search over the LKB. The authors apply a spread-
ing activation algorithm over the subgraph for
node ranking. Edges of the subgraph are weighted

according to its type, following a tf.idf like ap-
proach. The results show that our methods clearly
outperform Tsatsa07. The fact that the Spr method
works better suggests that the traditional PageR-
ank algorithm is a superior method for ranking the
subgraph nodes.
As stated before, all methods presented here
use some LKB for performing WSD. (Mihalcea,
2005) and (Sinha and Mihalcea, 2007) use Word-
Net relations as a knowledge source, but neither
of them specify which particular version did they
use. (Tsatsaronis et al., 2007) uses WordNet 1.7
enriched with eXtended WordNet relations, just
as we do. Both (Navigli and Velardi, 2005; Nav-
igli and Lapata, 2007) use WordNet 2.0 as the un-
derlying LKB, albeit enriched with several new
relations, which are manually created. Unfor-
tunately, those manual relations are not publicly
available, so we can’t directly compare their re-
sults with the rest of the methods. In (Agirre and
Soroa, 2008) we experiment with different LKBs
formed by combining relations of different MCR
versions along with relations extracted from Sem-
Cor, which we call supervised and unsupervised
relations, respectively. The unsupervised relations
that yielded bests results are also used in this paper
(c.f Section 3.1).
6 Experiments on Spanish
Our WSD algorithm can be applied over non-
english texts, provided that a LKB for this partic-

ular language exists. We have tested the graph-
algorithms proposed in this paper on a Spanish
dataset, using the Spanish WordNet as knowledge
source (Atserias et al., 2004a).
We used the Semeval-2007 Task 09 dataset as
evaluation gold standard (M
`
arquez et al., 2007).
The dataset contains examples of the 150 most
frequent nouns in the CESS-ECE corpus, manu-
Method Time
Ppr 26m46
Spr 119m7
Ppr
w2w 164m4
Table 4: Elapsed time (in minutes) of the algo-
rithms when applied to the Senseval-2 dataset.
ally annotated with Spanish WordNet synsets. It
is split into a train and test part, and has an “all
words” shape i.e. input consists on sentences,
each one having at least one occurrence of a tar-
get noun. We ran the experiment over the test part
(792 instances), and used the train part for cal-
culating the MFS baseline. We used the Span-
ish WordNet as LKB, enriched with eXtended
WordNet relations. It contains 105, 501 nodes and
623, 316 relations. The results in Table 3 are con-
sistent with those for English, with our algorithm
approaching MFS performance. Note that for this
dataset the supervised algorithm could barely im-

prove over the MFS, suggesting that for this par-
ticular dataset MFS is particularly strong.
7 Performance analysis
Table 4 shows the time spent by the different al-
gorithms when applied to the Senseval-2 all words
dataset, using the WNet17 + Xwn as LKB. The
dataset consists on 2473 word instances appear-
ing on 476 different sentences. The experiments
were done on a computer with four 2.66 Ghz pro-
cessors and 16 Gb memory. The table shows that
the time elapsed by the algorithms varies between
30 minutes for the Ppr method (which thus dis-
ambiguates circa 82 instances per minute) to al-
most 3 hours spent by the Ppr w2w method (circa
15 instances per minute). The Spr method lies
in between, requiring 2 hours for completing the
task, but its overall performance is well below the
PageRank based Ppr w2w method. Note that the
algorithm is coded in C++ for greater efficiency,
and uses the Boost Graph Library.
Regarding PageRank calculation, we have tried
different numbers of iterations, and analyze the
rate of convergence of the algorithm. Figure 1 de-
picts the performance of the Ppr
w2w method for
different iterations of the algorithm. As before, the
algorithm is applied over the MCR17 + Xwn LKB,
and evaluated on the Senseval-2 all words dataset.
The algorithm converges very quickly: one sole it-
eration suffices for achieving a relatively high per-

39
57
57.2
57.4
57.6
57.8
58
58.2
58.4
58.6
0 5 10 15 20 25 30
Recall
Iterations
Rate of convergence
✸
✸
✸
✸ ✸
✸ ✸ ✸
Figure 1: Rate of convergence of PageRank algo-
rithm over the MCR17 + Xwn LKB.
formance, and 20 iterations are enough for achiev-
ing convergence. The figure shows that, depend-
ing on the LKB complexity, the user can tune the
algorithm and lower the number of iterations, thus
considerably reducing the time required for disam-
biguation.
8 Conclusions
In this paper we propose a new graph-based
method that uses the knowledge in a LKB (based

on WordNet) in order to perform unsupervised
Word Sense Disambuation. Our algorithm uses the
full graph of the LKB efficiently, performing bet-
ter than previous approaches in English all-words
datasets. We also show that the algorithm can be
easily ported to other languages with good results,
with the only requirement of having a wordnet.
Both for Spanish and English the algorithm attains
performances close to the MFS.
The algorithm is publicly available
5
and can be
applied easily to sense inventories and knowledge
bases different from WordNet. Our analysis shows
that our algorithm is efficient compared to previ-
ously proposed alternatives, and that a good choice
of WordNet versions and relations is fundamental
for good performance.
Acknowledgments
This work has been partially funded by the EU Commission
(project KYOTO ICT-2007-211423) and Spanish Research
Department (project KNOW TIN2006-15049-C03-01).
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